Lin / Zhao Handbook on Analyzing Human Genetic Data

Computational Approaches and Software
1. Auflage 2009
ISBN: 978-3-540-69264-5
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 333 Seiten

The discipline of statistical genetics is highly computational. Be it exact compu- tionalmethods,simulationbased,orahybridofthetwo,computationalpackagesare indispensable tools and constant companions of researchers in the ?eld. This ha- bookisintendedtoprovidehumangeneticistsandotherbiomedicalresearcherswith guidance on selections of appropriate computational methods and software pa- ages for their speci?c genetic problems. It may also be used by students and other learners as a reference in conjunction with a more theoretical and/or methodolo- cally oriented text book. This book tries to strike a balance between methodological expositions and practical guidelines for software selections. Wherever possible, comparisons among the competing methods and software are made to highlight the relativeadvantagesanddisadvantagesoftheapproaches,so thatthe readercanmake informed choices to best match their speci?c needs. Human genetics has been undergoing an evolution in the past several years as new knowledge and technologies are transforming the ?eld, leading to numerous new discoveriesof genes associated with complex traits such as cancer, obesity, and diabetes. Many recent genome-wide association studies employ the case–control design, where the study subjects consist of unrelated affected individuals and n- mal controls. For each individual, a large number of genetic markers are queried.

Autoren/Hrsg.

Lin, Shili

Zhao, Hongyu

Inhaltsverzeichnis

1;Preface and Introduction;5
2;Contents;10
3;Population Genetics;14
3.1;1 Introduction;14
3.2;2 Within-Population Analyses;15
3.2.1;2.1 Genotype and Allele Frequencies;15
3.2.2;2.2 Maximum Likelihood Estimation;16
3.2.3;2.3 Inbreeding Coefficient;17
3.2.4;2.4 Testing for Hardy–Weinberg Equilibrium;19
3.2.5;2.5 Linkage Disequilibrium;21
3.2.6;2.6 Composite Linkage Disequilibrium;22
3.2.7;2.7 Testing for Linkage Equilibrium;23
3.2.8;2.8 Application to Data;24
3.3;3 Between-Population and Analyses;29
3.3.1;3.1 F-statistics;29
3.3.2;3.2 Application to Data;33
3.4;4 Discussion;35
3.5;5 Web Resources;35
3.6;References;36
4;Haplotype Structure;37
4.1;1 Population Haplotype Structure;37
4.1.1;1.1 Haplotype Block Structure in Human Populations;37
4.1.2;1.2 Wright–Fisher Model;38
4.1.3;1.3 Coalescent Theory;40
4.2;2 Public Genotype/Haplotype Databases;42
4.2.1;2.1 International HapMap Project;43
4.2.1.1;Download Genotype Data from HapMap;44
4.2.2;2.2 The HapMap ENCODE Resequencingand Genotyping Project;45
4.2.2.1;2.2.1 Download ENCODE Genotype Data;46
4.2.3;2.3 Haplotype Simulation;46
4.3;3 Haploview;48
4.3.1;3.1 What is Haploview?;48
4.3.2;3.2 How to Download and Install Haploview;48
4.3.3;3.3 How to Run Haploview;49
4.3.3.1;3.3.1 How to Use HapMap Data in Haploview;49
4.3.3.2;3.3.2 How to Use Non-HapMap Genotype Data in Haploview;50
4.4;4 Haplotype Inference Methods;53
4.4.1;4.1 Clark's Algorithm;54
4.4.1.1;4.1.1 Software Usage;55
4.4.2;4.2 PHASE;58
4.4.2.1;4.2.1 PHASE Algorithm;59
4.4.2.2;4.2.2 Software Usage;60
4.4.3;4.3 HAPLOTYPER;65
4.4.3.1;4.3.1 Bayesian Model;65
4.4.3.2;4.3.2 Partition–Ligation (PL);66
4.4.3.3;4.3.3 Software Usage;68
4.4.4;4.4 CHB;70
4.4.4.1;4.4.1 CHB Model;70
4.4.4.2;4.4.2 MCMC Sampling and Convergence;72
4.4.4.3;4.4.3 Software Usage;73
4.4.5;4.5 Comparison of Phasing Results;76
4.5;5 Estimation of Recombination Rate;76
4.5.1;5.1 LDhat;77
4.5.1.1;5.1.1 Composite Likelihood Estimation of ;77
4.5.1.2;5.1.2 Likelihood Permutation Test;78
4.5.1.3;5.1.3 Software Usage;79
4.5.2;5.2 HOTSPOTTER;83
4.5.2.1;5.2.1 PAC Model;83
4.5.2.2;5.2.2 Computing the Conditional Distribution ;85
4.5.2.3;5.2.3 Software Usage;86
4.6;Summary;88
4.7;Web Resources;89
4.8;References;89
5;Linkage Analysis of Qualitative Traits;92
5.1;1 Introduction;92
5.2;2 Model-Based Linkage Analysis;93
5.2.1;2.1 Phase-Known Pedigrees;93
5.2.2;2.2 Phase-Unknown Pedigrees;96
5.2.3;2.3 Linkage Analysis in General Case;98
5.2.4;2.4 Elston–Stewart Algorithm;99
5.3;3 Model-Free Linkage Analysis;101
5.3.1;3.1 Fundamental Principle of Model-Free Linkage Analysis;101
5.3.2;3.2 Measure of Genetic Similarity;102
5.3.3;3.3 Model-Free Linkage Analysis for Affected Sib Pairs;103
5.3.4;3.4 Multipoint Analysis for Affected Sib Pairs;106
5.3.5;3.5 Model-Free Linkage Analysis for General Pedigrees;108
5.3.5.1;3.5.1 Inheritance Vector;108
5.3.5.2;3.5.2 NPL Score When the Inheritance Vector Is Known;109
5.3.5.3;3.5.3 NPL Score When the Inheritance Vector Is Uncertain;110
5.3.6;3.6 Lander–Green Algorithm;111
5.4;4 Practical Examples;113
5.5;5 Identifying SNPs Responsible for a Linkage Signal;117
5.5.1;5.1 Assumptions and Definitions;117
5.5.2;5.2 Conditional Probability of Marker Data Given ASP;118
5.5.3;5.3 Relationship Between Disease Locus and Candidate SNP;119
5.5.4;5.4 Hypothesis Testing;120
5.5.5;5.5 Extension to Sibship Data and Nuclear Families;122
5.5.6;5.6 Summary;124
5.6;6 Comparison of Model-Based and Model-Free Linkage Analysis Methods;124
5.6.1;6.1 Software Packages for Linkage Analysis;126
5.7;Web Resources;126
5.8;References;127
6;Linkage Analysis of Quantitative Traits;130
6.1;1 Introduction and Description of Data;130
6.2;2 Methods;132
6.2.1;2.1 Classical Model-Based Linkage Analysis;134
6.2.2;2.2 Model-Free Haseman–Elston Regression Approach;138
6.2.3;2.3 Variance-Components Approaches;139
6.2.4;2.4 Model-Free Variance Regression;145
6.2.5;2.5 Multivariate Models;147
6.2.6;2.6 Joint Linkage and Association Analysis;149
6.3;3 Discussion;149
6.4;4 Web Resources;151
6.5;References;152
7;Markov Chain Monte Carlo Linkage Analysis Methods;157
7.1;1 Introduction;157
7.2;2 Test Data;159
7.2.1;2.1 Data from the Framingham study;159
7.2.2;2.2 Simulated data;160
7.3;3 MCMC Methods and Packages;161
7.4;4 Comparison of Methods;162
7.4.1;4.1 Analysis Strategies;162
7.4.1.1;4.1.1 Estimation of Segregation Models for TH;163
7.4.1.2;4.1.2 Linkage Analysis Based on Loki;164
7.4.1.3;4.1.3 Linkage Analysis Based on MORGAN;164
7.4.1.4;4.1.4 Linkage Analysis Based on SimWalk2;164
7.4.2;4.2 Comparison of the Three Linkage Analysis Software;165
7.4.2.1;4.2.1 Framingham Data;165
7.4.2.2;4.2.2 Simulated Data;169
7.5;5 Conclusions, Recommendations, and Other Considerations;173
7.6;6 Web Resources;177
7.7;References;177
8;Population-Based Association Studies;180
8.1;1 Introduction;180
8.2;2 The Data;181
8.2.1;2.1 Association of a Genetic Marker and a Disease;182
8.2.2;2.2 Testing for Association When No PopulationStratification Is Present;184
8.2.3;2.3 False Positive Can Be Aroused When PopulationStratification Is Present;186
8.3;3 Genome-Control Approach;186
8.4;4 Structured Association Approach;187
8.5;5 Methods Based on Principal Components (PC);189
8.5.1;5.1 Mixture Model;190
8.5.2;5.2 Semi-Parametric Approach;192
8.5.3;5.3 Linear Model Approach;194
8.6;6 Discussion;195
8.7;Web Resources;196
8.8;References;197
9;Family-Based Association Studies;200
9.1;1 Introduction;201
9.2;2 Basic Notations;202
9.3;3 Qualitative Traits, Trios, Bi-Allelic Markers;203
9.3.1;3.1 Qualitative Traits, Trios, Multi-Allelic Markers;204
9.4;4 Family with Multiple Siblings;207
9.5;5 Families with Missing Parental Genotypes;210
9.6;6 Quantitative Phenotypes;217
9.7;7 Joint Analysis of Multiple Markers;221
9.8;8 Other Association Methods Using Family-Based Designs;228
9.8.1;8.1 General Pedigrees;228
9.8.2;8.2 Gene–Gene (GG) interaction and Gene–Environment (GE) Interaction;230
9.9;9 Software Packages and Power Consideration;231
9.10;10 Discussion;236
9.11;References;240
10;Haplotype Association Analysis;250
10.1;1 Introduction;250
10.1.1;1.1 The FUSION Study;252
10.1.2;1.2 General Notation;253
10.2;2 Haplotype Analysis of Unrelated Samples;253
10.2.1;2.1 Cross-Sectional Studies;253
10.2.1.1;2.1.1 Analyses Using Phased Haplotypes;253
10.2.1.2;2.1.2 Analyses Using Unphased Haplotypes;255
10.2.1.3;2.1.3 Stability Issues in Haplotype Analysis;256
10.2.1.4;2.1.4 Modeling Interaction Effects;257
10.2.1.5;2.1.5 Haplotype Clustering;257
10.2.1.6;2.1.6 Software Packages;259
10.2.1.7;2.1.7 Software Application to FUSION Data;261
10.2.2;2.2 Cohort Studies;263
10.2.2.1;2.2.1 Software Packages;264
10.2.3;2.3 Case–Control Studies;264
10.2.3.1;2.3.1 Related Study Designs;267
10.2.3.2;2.3.2 Haplotype Similarity Analyses;268
10.2.3.3;2.3.3 Software Packages;270
10.2.3.4;2.3.4 Software Application to FUSION Data;271
10.3;3 Haplotype Analysis of Family-based Samples;273
10.3.1;3.1 Haplotype Approach of Horvath et al. ;274
10.3.2;3.2 Haplotype Approach of Allen and Satten ;276
10.3.3;3.3 Software Packages;279
10.4;4 Summary;280
10.5;Electronic-Database Information;281
10.6;References;282
11;Multiple Comparisons/Testing Issues;286
11.1;1 Introduction;286
11.2;2 Bonferroni Correction;287
11.3;3 False Discovery Rate;288
11.4;4 Randomization Testing;289
11.5;5 Single Experiment-Wise Test Statistic;291
11.6;6 Example Dataset: Parkinson Disease;292
11.7;7 Discussion;294
11.8;Web Resources;295
11.9;References;295
12;Estimating the Absolute Risk of Disease Associated with Identified Mutations;297
12.1;1 Introduction;297
12.2;2 Population-Based Cohort Studies;300
12.3;3 Case–Control Designs;302
12.4;4 Case–Control Family Study Design;303
12.5;5 Kin–Cohort Design;308
12.6;6 Discussion;312
12.7;References;312
13;Processing Large-Scale, High-Dimension Genetic and Gene Expression Data;314
13.1;1 Introduction;314
13.2;2 Data Management, Access and Workflow;316
13.3;3 Analysis Issues with High-Dimensional Data;318
13.3.1;3.1 Power;318
13.3.2;3.2 Data Trends and Unaccounted for Heterogeneity;320
13.3.3;3.3 Outliers and Transformations;320
13.4;4 Implementing a Standard First-Pass Analysis Pipeline;321
13.4.1;4.1 The Model – Common vs. Individual;321
13.4.2;4.2 Estimating Heritability;322
13.4.3;4.3 Ethnicity and Substructure;323
13.4.4;4.4 Multiplicity;323
13.5;5 High-Performance Computing;324
13.6;6 Further Recommendations for Efficiency Gainsin GOGE Studies;326
13.7;7 Constructing Gene Networks to Enhance GWASand GOGE Results;327
13.7.1;7.1 Constructing Weighted and UnweightedCo-Expression Networks;328
13.7.2;7.2 Using Genetics in Constructing Co-Expression Networks;329
13.7.3;7.3 Identifying Modules of Highly Interconnected Genes in Co-Expression Networks;329
13.8;8 Looking Toward the Future: Probabilistic Causal Networks;331
13.9;9 Summary;332
13.10;Web Resources;333
13.11;References;334
14;Index;338